Combing Remotely Sensed Data and Health Surveys to Investigate Food Insecurity and Women’s Reproductive Health in West Africa

Abstract:

In low-income farming communities in West Africa where populations are growing rapidly, climate change is anticipated to reduce agricultural production and increase household food insecurity. The impact on women’s health of more frequent and severe periods of food insecurity is anticipated to significantly elevate women’s risks of adverse and irreversible health outcomes. In particular, women of reproductive age are likely to face adverse pregnancy and childbearing outcomes because of biological and behavioral factors associated with food insecurity. Quantitative evidence linking reproductive health outcomes and food security is limited by lack of spatial analyses that consider individual-level women’s health outcomes. Expanding the body of quantitative evidence that clearly links food insecurity experiences to individual- and community-level health outcomes is vital for informing effective aid planning and climate change mitigation strategies. The goal of this research is to uncover the linkages between women’s reproductive health outcomes and changes in short-term food production. We use highly detailed, spatially-referenced reproductive health information, capturing the experiences of more than 10,000 women from the Gates Foundation’s Performance Monitoring for Action (PMA) data collected for Burkina Faso, Niger and Cote D’Ivoire in 2017 and 2018. The data contain monthly retrospective information on reproductive health outcomes including pregnancy, pregnancy aspirations, and infant health outcomes – biological and behavioral health outcomes that are anticipated to vary according to seasonal and annual food production. We combine these data with fine-scale remotely sensed based data used to capture food production anomalies as estimated using vegetation indices such as NDVI Sentinel and WRSI, over relevant recent growing seasons. To quantify the linkages, we use flexible, spatial Generalized Additive Models (GAMs) that accommodate the potential for non-linear relationships between the key independent variables and the outcome variables. Preliminary results suggest that poor growing season conditions related to food insecurity significantly increase contraceptive use.